library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5 ✓ purrr 0.3.4
✓ tibble 3.1.6 ✓ dplyr 1.0.6
✓ tidyr 1.1.3 ✓ stringr 1.4.0
✓ readr 1.4.0 ✓ forcats 0.5.1
── Conflicts ────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
library(plotly)
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching package: ‘plotly’
The following object is masked from ‘package:ggplot2’:
last_plot
The following object is masked from ‘package:stats’:
filter
The following object is masked from ‘package:graphics’:
layout
library(rcartocolor)
merged <- read_tsv('../output/merged_table.tsv')
── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
Specialty = col_character(),
`No. of Programs` = col_double(),
`Positions Offered` = col_number(),
`Unfilled Programs` = col_double(),
`MD Senior Applicants` = col_number(),
`Total Applicants` = col_number(),
`MD Senior Matches` = col_number(),
`Total Matches` = col_number(),
`MD Senior % Filled` = col_double(),
`Total % Filled` = col_double(),
`MD Senior Ranked Positions` = col_number(),
`Total Ranked Positions` = col_character(),
Year = col_double(),
Class = col_character()
)
merged %>%
filter(Specialty == 'Dermatology')
long_table_absolute <- merged %>%
select(Specialty,
Year,
Class,
`Positions Offered`,
`MD Senior Applicants`,
`Total Applicants`,
`MD Senior Matches`,
`Total Matches`) %>%
pivot_longer(cols = -c(Specialty, Year, Class),
names_to = 'Name',
values_to = 'Value') %>%
arrange(Specialty, Class, Year)
long_table_absolute
long_table_percent <- merged %>%
select(Specialty,
Year,
Class,
`MD Senior % Filled`,
`Total % Filled`) %>%
pivot_longer(cols = -c(Specialty, Year, Class),
names_to = 'Name',
values_to = 'Value') %>%
arrange(Specialty, Class, Year)
long_table_percent
p <- long_table_absolute %>%
filter(Specialty == 'Dermatology', Class == 'PGY2') %>%
ggplot(aes(x = Year,
y = Value,
color = Name)) +
geom_point() +
geom_line() +
facet_wrap(~Specialty + Class, scales = 'free_y') +
theme_bw() +
scale_color_carto_d(palette = 'Bold')
p

input <- 'Dermatology'
plot_data <- merged %>% filter(Specialty == input,
Class == 'PGY2')#long_table_absolute %>% filter(Specialty == 'Dermatology')
fig <- plot_ly(plot_data, x = ~Year)
fig <- fig %>% add_trace(y = ~`Positions Offered`, name = 'Positions Offered', mode = 'lines+markers')
fig <- fig %>% add_trace(y = ~`MD Senior Applicants`, name = 'MD Senior Applicants', mode = 'lines+markers')
fig <- fig %>% add_trace(y = ~`Total Applicants`, name = 'Total Applicants', mode = 'lines+markers')
fig %>%
layout( # all of layout's properties: /r/reference/#layout
title = input, # layout's title: /r/reference/#layout-title
xaxis = list( # layout's xaxis is a named list. List of valid keys: /r/reference/#layout-xaxis
title = "Time", # xaxis's title: /r/reference/#layout-xaxis-title
showgrid = F), # xaxis's showgrid: /r/reference/#layout-xaxis-showgrid
yaxis = list( # layout's yaxis is a named list. List of valid keys: /r/reference/#layout-yaxis
title = "uidx") # yaxis's title: /r/reference/#layout-yaxis-title
)
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatter
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatter
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatter
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatter
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatter
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatter
p <- long_table_absolute %>%
filter(Specialty == 'Dermatology') %>%
ggplot(aes(x = Year,
y = Value,
color = Name)) +
geom_point() +
geom_line() +
facet_wrap(~Specialty + Class, scales = 'free_y') +
theme_bw() +
scale_color_carto_d(palette = 'Bold')
ggplotly(p)
Export
long_table_absolute %>% write_tsv('../../analysis/output/long_table_absolute.tsv')
long_table_percent %>% write_tsv('../../analysis/output/long_table_percent.tsv')
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